Master in Business Analytics


Program Description

Application deadline update

  • Non-EU/EEA students: 1 May (the original deadline of 1 April has been extended due to COVID-19)
  • EU/EEA students and students who do not need a study visa/residence permit through VU Amsterdam*: 1 June (the original deadline of 1 May has been extended due to COVID-19)

Find expert solutions to today's complex business problems
In today’s data driven world, organizations need expert judgement and sharp analysis to ensure they make the right decisions. Business Analytics enables you to harness the power of data science, big data, statistics and machine learning to optimize results and achieve strategic objectives.

Your ability to combine insights from mathematics, computer science and economics with highly developed quantitative and communication skills will make you key to the success of any organization. The Master’s programme will deepen your knowledge in these areas and give you the opportunity to specialize in computational intelligence, business process optimization, and financial risk management. The Master’s degree is concluded with a six-month individual internship at a company, which is often the first step to a thriving international career.

This is what you will be doing
The Master’s programme in Business Analytics is a two year programme consisting of 120 ECTS, divided into four semesters. The first semester is devoted to compulsory courses. During the second and third semester, you may focus on one of the three specializations. Finally, in the fourth semester, you spend six months on an internship at a business or research institute.

Compulsory courses 42 EC
At least four out of sixteen optional BA courses 24 EC
Other optional courses 18 EC
Master's project 36 EC

You will learn:

  • The art of mathematical modelling and ways to apply it in practice
  • To build a complete decision support system in a group project
  • To analyze data and recognize data patterns and structures
  • Mathematical methods used within financial institutions
  • Scientific writing and presentation skills
  • To work in an interdisciplinary team, to communicate and work together with experts in different areas
  • To combine and apply your knowledge in practice by means of a traineeship

Compulsory courses
The compulsory courses are:

  • Applied Stochastic Modelling (6 EC)
    This course provides you with an insight into mathematical modelling and the way it is used in practice. You will explore a number of stochastic solution methods.
  • Data-Mining Techniques (6 EC)
    This course surveys basic data-mining techniques and their application in solving real-life problems in such areas as marketing, fraud detection, text and web mining, and bioinformatics.
  • Applied Analysis: Financial Mathematics (6 EC)
    This course introduces you to the maths used within financial institutions. Topics covered include the theory of options, the binomial method, the Black-Scholes model and its application, the heat equation, and numerical methods.
  • Project Optimization of Business Processes (group project, 6 EC)
    This project concerns the construction and/or design of (part of) a decision support system (DSS) that is designed and built in a scientifically sound way, and can be used in practice. The DSS is built in groups of students.
  • Statistical Models (6 EC)
    In this course you will learn to apply several common statistical models in valid settings, and will learn the theoretical foundation for each model. Topics that will be discussed are: analysis of variance, generalized linear models, non-linear models and time series models.
  • Research Seminar Business Analytics (6 EC)
    During (for instance) the master project the student should consider relevant literature for the research question at hand. This course aims to prepare the student for this step. Getting acquinted with the literature consists of two elements: (i) to be able to study a specific paper in depth, and (ii) to carry out a desk research on scientific literature (sort of review) related to a research question. Both of these elements are addressed during the seminar.
  • Advanced Machine Learning (6 EC)
    Machine learning is the science of getting computers to act without being explicitly programmed. In this course, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work yourself. We will discuss the theoretical underpinnings as well as the practical know-how needed to apply these techniques to new problems.

During the second and third semester you may specialize in one direction of expertise. The three main specializations are:

  • Business process optimization
  • Computational intelligence
  • Financial risk management
Last updated Feb 2020

About the School

Vrije Universiteit Amsterdam is an internationally renowned research university founded in 1880. The university offers over 150 English taught programmes at Bachelor’s, Master’s and PhD level to 23,00 ... Read More

Vrije Universiteit Amsterdam is an internationally renowned research university founded in 1880. The university offers over 150 English taught programmes at Bachelor’s, Master’s and PhD level to 23,000 students from all over the world. Students and staff of 122 nationalities create a dynamic international academic community. The University distinguishes itself in research and education through four interdisciplinary themes: Human Health and Life Sciences, Science for Sustainability, Connected World and Governance for Society. Curious about student life at VU Amsterdam? Click here to ask our International Student Ambassadors. Read less